1 Introduction

People drink coffee for various reasons: Either to warm their soul in a chilling morning, or to stay awake and focus after a long, restless night. Whatever the reason is, it is clear that we love the dark, fragrance liquid. Some people loves coffee for its pleasant aroma. Some others loves its unique flavor notes. Some simply loves it because it is as black as their soul. But most would agree that nothing is better than a cup of great coffee to start a wonderful, productive day.

Great coffee beans produce great coffee cup. The question is, what do we, coffee lovers, should consider when planning to buy a premium bag of the charming black beans? Hence, in this report. We will have a journey to the place where the coffee trees were raised and the beans were harvested, to see what affects the quality of our beloved coffee.

2 Data description

2.1 Data source

The Coffee Quality Institute is a non-profit organization that grades coffee samples from around the world in a consistent and professional manner.

The coffee beans are graded by the Coffee Quality Institute’s trained reviewers. The total rating of a coffee bean is a cumulative sum of 10 individual quality measures: aroma, flavour, aftertaste, acidity, body, balance, uniformity, clean cup, sweetness and cupper points. Each grade is on a 0–10 scale resulting to a total cupping score between zero and one hundred.

2.2 Dataset source

The data is originally observed from Coffee Quality Institute website and was scraped by James DeLoux, the data was then re-posted on Kaggle. Furthermore, it is then cleaned by Thomas Mock.

2.3 Data limitations

The cleaned data set consists of 1339 observations and 43 variables. There are some potential limitations are:

  • As the number of graded coffee beans differ largely from country to country, some of the analysis will be biased.

  • For US, there are 3 areas that produce coffee beans: Mainland, Puerto Rico and Hawaii. In this research, the researchers merge all this areas together to better represent the country.

  • There are outliers in data, which may require further cleaning before certain analysis can take place.

  • There are difference in scale system in the data. In detail, it is unclear whether the altitude of a farm was converted from meter to feet.

2.4 Data cleaning and wrangling

2.4.1 Original cleaning

Data wrangling and cleaning is crucial to produce an exploratory data analysis fluently. The original data is a data frame scraped by James LeDoux in January 2018 from the Coffee Quality Institute website which has a few missing values columns within it, so the author has cleaned the data set by removing the variables: “view_certificate_1”, “view_certificate_2”,etc. On the other hand, there are two separate data set raw_robusta and raw_arabica originally. Thomas Mock first cleaned the variable names in both data set with function janitor::clean_names, and inappropriate data class is corrected using col_double, col_character etc. Variables like salt_acid, bitter_sweet, fragrance_aroma, mouthfeel, and uniform_cup is renamed to acidity, sweetness, aroma, body and uniformity respectively, to allow a better understanding for readers.

The data sets were then joined by implementing the function bind_rows to produce the merged data set, which is exported to a single csv file “coffee_ratings.csv” with 1339 observations and 43 variables.

2.4.2 Further data cleaning

Although the dataset was properly cleaned and wrangled by the original author, there are still some issues in the dataset:

  1. There are anomalies in the data. In detail, there are some coffee beans were grown at the height of more than 11,000 meters. Given that the highest point on Earth is only 8,848 meters above sea level, those observations are inaccurate.

  2. There are several missing values in the dataset. In order to be more accurate at our research, we tried to fill some critical information that can affect our data.

Based on this, the data set was cleaned again to produce the most accurate analysis possible.

2.5 Structure of data

After this knowing what each of those variables define with respect to our topic is important so below is the description of variables included in the data set:

Variable Class Description
total_cup_points double Total rating/points (0 - 100 scale)
species character Species of coffee bean (arabica or robusta)
owner character Owner of the farm
country_of_origin character Where the bean came from
farm_name character Name of the farm
lot_number character Lot number of the beans tested
mill character Mill where the beans were processed
ico_number character International Coffee Organization number
company character Company name
altitude character Altitude - this is a messy column - I’ve left it for some cleaning
region character Region where bean came from
producer character Producer of the roasted bean
number_of_bags double Number of bags tested
bag_weight character Bag weight tested
in_country_partner character Partner for the country
harvest_year character When the beans were harvested (year)
grading_date character When the beans were graded
owner_1 character Who owns the beans
variety character Variety of the beans
processing_method character Method for processing
aroma double Has both fragrance (ground beans) and aroma (hot water with coffee powder)
flavor double Flavor grade
aftertaste double Length of positive flavor remaining after the coffee is swallowed
acidity double The score depends on the origin characteristics and other factors(degree of roast)
body double Body grade
balance double Balance grade
uniformity double Refers to the consistency of flavor . 2 points are awarded for each cup displaying this attribute, with a maximum of 10 points if all 5 cups are the same.
clean_cup double Refers to a lack of interfering negative impressions from first ingestion to final aftertaste
sweetness double Sweetness grade
cupper_points double The cupper marks the intensity of the Aroma on a scale
moisture double Moisture Grade
category_one_defects double Full black or sour bean, pod/cherry, and large or medium sticks or stones(count)
quakers double Unripened beans that are hard to identify during hand sorting and green bean inspection
color character Color of bean
category_two_defects double Parchment, hull/husk, broken/chipped, insect damage, partial black or sour, shell, small sticks or stones, water damage(count)
expiration character Expiration date of the beans
certification_body character Who certified it
certification_address character Certification body address
certification_contact character Certification contact
unit_of_measurement character Unit of measurement
altitude_low_meters double Altitude low meters
altitude_high_meters double Altitude high meters
altitude_mean_meters double Altitude mean meters

2.6 Questions of interest

The aim of this report is to discover the likely factors that influence coffee quality and taste.

Secondary question:

  1. Which Country produces the best quality coffee beans? Are there any large difference in coffee quality worldwide?

  2. Do factors like altitude, processing method and defects affect the quality of the coffee?

  3. Are coffee graders consistent with their grading across years, months and days of the week? (NEW)

  4. What is the common processing method of top-graded coffee beans, how different processing method behaves on the individual grading criteria of coffee? (NEW)

3 Analysis and Findings

3.1 Which Country produces the best quality coffee beans?

Figure 3.1 aims to address the primary question Which country produces best quality coffee beans?. X axis shows the country while Y axis denotes the overall rating achieved by the coffee bean. It is clear that Ethiopia produced the highest quality of coffee beans. However, it is interesting to note that there is not much variation between countries as most of them have median score of around 80-85 points. Thus, We can conclude that based on the dataset, there is not much difference in coffee quality between countries, with Ethipoia produces the highest-quality beans.

Figure 3.1: Boxplot for total ratings of coffee beans of country with at least 5 coffee samples.

3.2 What other factors that affect the quality of coffee beans?

The dataset contains information about the coffee sample’s altitude of origin, variety, processing method and color. In this section, I examine if any of these characteristics correlate with the total cupping score.

3.2.1 Altitude v/s Quality

In this section, our team ran a model of the altitude of the sample and its total cupping score. The result is displayed in table 3.1 and figure 3.2. The p-value is lower then 0.05 which indicates that the model is statistically significant. The result shows a positive relationship between altitude and quality of coffee beans produced. However, it is worth to notice that the Adjusted R Square of the model is extremely small, only 0.022, or 2% (See table 5.1, Appendix 1), which suggest that the model is very weak. Therefore, I tried another method which is Pearson correlation coefficient to examine the relationship between altitude and cupping points. As suggested by table 3.2, there are no significant correlation.

There are some very interesting findings from 3.2. For example, it seems that the majority of coffee samples were grown 750-2000m above sea levels. Also, the most of coffee beans seems to score very high,around 80-85 points.

Table 3.1: Altitude v/s Quality stats
term estimate std.error statistic p.value
(Intercept) 81.09426 0.22743 356.56384 0
altitude_mean_meters 0.00082 0.00016 5.11726 0
Altitude and Total cup points dot plot, blueline presents fitted regression model, redlines indicate present altitude of 750m and 2000m.

Figure 3.2: Altitude and Total cup points dot plot, blueline presents fitted regression model, redlines indicate present altitude of 750m and 2000m.

Table 3.2: Altitude v/s Quality stats
estimate statistic p.value parameter conf.low conf.high method alternative
-0.0201263 -0.6694689 0.5033361 1106 -0.0789258 0.0388127 Pearson’s product-moment correlation two.sided

3.2.2 Processing Method v/s Quality

To check if the processing method affects the quality of coffee beans produced, we have taken help of the ANOVA test as the processing method is a categorical variable. The ANOVA test returns p-values very far away from the confidence interval of 5% which can be observed when we plot the residuals against the fitted values in Figure 1 . Hence it is established that the processing methods used in producing the coffee beans does not influence the quality of beans produced.

##               Df Sum Sq Mean Sq F value Pr(>F)  
## method         4     58  14.468   1.978 0.0956 .
## Residuals   1164   8514   7.314                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 169 observations deleted due to missingness
ANOVA test for processing methods vs Quality

Figure 3.3: ANOVA test for processing methods vs Quality

3.2.3 Defects v/s Quality

After the linear model for altitude turned out to be insignificant, we figured there are several other variables in the dataset that we could try fitting a model. The dataset contains category one and category two defects which are also known as primary and secondary defects and we fitted a muti-variate model using the same. The model after considering both the variables return a p-value very close to 0 and hence this model is considered a good one and as can be seen in Figure 2 which suggest that almost all the residuals reside very close to the 0 line with a very few outliers. Thus it can be understood that defects influence the quality of coffee beans produced.

Table 3.3: Defects linear regression model
term estimate std.error statistic p.value
(Intercept) 82.5885115 0.1122747 735.593003 0.00000
category_one_defects -0.1106967 0.0378995 -2.920796 0.00355
category_two_defects -0.1252918 0.0181894 -6.888192 0.00000

3.3 What is the trend in grading date?

While waiting for my lovely cup of coffee to brew, one of the researcher happened to read a interesting research Danziger, Levav, and Avnaim-Pesso (2011) that has found correlation between a parole case’s success rate and whether the case was heard right before or after lunch. The idea is that judges might be more exhaused before lunch, which would make them stricter. Enlightened, he wanted to see if there is anything similar happen in coffee grading. Are graders stricter on Friday compared to Monday?

Figure 3.4: Mean total cup scores by week day. The average point stay stable throughout the week

The answer seems to be No.

From figure 3.4, it seems that coffee graders seem to be consistent with their grading accross the weeks.

But, does this trend stay true accross years and months? Figure 3.5 answer that question. The figure suggest that the score remain stable throughout months and years.

From the previous findings, the researchers conclude that coffee graders seem to be consistent with their grading across years, months and days of the week. On that ground, it is safe to say that coffee seems to be consistent in quality throughout the years.

Mean total cup score per grading year and month

Figure 3.5: Mean total cup score per grading year and month

3.4 What is the common processing method of top-graded coffee beans, how different processing method behaves on the individual grading criteria of coffee? (NEW)

Processing Method amongst best-graded coffee beans

Figure 3.6: Common processing method of top 30 coffee beans by country

Processing Method Frequency
Natural / Dry 11
Pulped natural / honey 1
Semi-washed / Semi-pulped 1
Washed / Wet 17

The barchart 3.6 demonstrates the frequency of processing method in top 30 coffee bean producers, we discovered that the producers in most countries is using washed/wet method to produce coffee beans, followed by natural/dry method. In addition, majority of coffee bean producers in Ethiopia, the country that produces highest quality coffee beans, is using the natural/dry method. This is explained by Korhonen (2020), “The natural process is common in regions where there is no access to water such as Ethiopia and some regions in Brazil.”

Behaviour of different processing method on the individual grading criteria of coffee

We picked 6 grading criteria, according to the coffee scoring article provided on mycuppa:

  • Aroma
  • Aftertaste
  • Flavour
  • Acidity
  • Body
  • Balance
Scatterplot matrix of grading criterias, differentiated by processing method

Figure 3.7: Scatterplot matrix of grading criterias, differentiated by processing method

The scatterplot matrix 3.7 examines the relationship between each grading criteria, and is differentiated by the processing method.

  • The scatterplot matrix demonstrates the correlation of grading criteria are positive to various extent.

  • The density plot for acidity located on 1st row & 1st column indicates the method “Pulped natural/honey” have relatively low density compared to other processing method.

  • One thing to notify is that there are presence of outliers for “Washed/Wet” method in aroma grade (on 3rd column).

  • A bunch of coffee beans processed with “Washed/Wet” method have relatively lower aftertaste grade.

Overall, we can observe that the “Washed/Wet” method have relatively more variations compared to other method, and the plot 3.7 also indicates the primary processing method are “Washed/Wet” and “Natural/Dry” across all coffee bean producers.And we can barely capture the appearance of other two methods.

3.5 Bonus: Country’s coffee profile

In this section, we provide radar charts of five countries from accross the world: Ethiopia, Unitd Statets, Brazil, Peru and Indonesia. A radar chart is a useful way to depict multi-variate observations. Each criteria is rated out of a total 10 points and all the 10 criteria are plotted together on the radar chart along with moisture percentage to understand how a particular country performs on individual criteria.

Table 3.4: Means for different Individual grading criterias
country ma mfl maf mac mb mba mu mc ms mcu mm
Brazil 7.553106 7.573561 7.440530 7.511439 7.544545 7.531515 9.884924 9.853485 9.949394 7.562955 0.0823485
Ethiopia 7.896364 8.009091 7.893864 8.043636 7.924091 7.972273 9.878409 9.954318 9.863409 8.048636 0.0829545
Indonesia 7.649000 7.573000 7.415500 7.491000 7.631500 7.520000 9.933000 9.966500 9.966500 7.416500 0.0985000
Peru 7.693000 7.658000 7.541000 7.699000 7.802000 7.666000 9.533000 9.466000 9.866000 7.601000 0.0660000
US 7.605977 7.622529 7.541609 7.651609 7.652414 7.664138 9.501379 9.608966 9.643218 7.623448 0.0509195

After looking at these plots, the conclusion drawn are as follows: The common characteristics that these top 5 countries have are the consistent higher values of uniformity and clean cup. Among all these countries, it can be seen that the country Ethiopia has the highest values for all the different characteristics that we have proven to have a significant affect on the quality of the coffee beans in the above sections. It is also interesting how the sweetness has a perfect score of 10 in all other countries other than United States as depicted.

4 Conclusion

5 Appendix

5.1 Appendix 1. Summary of altitude model

Table 5.1: Altitude v/s Quality, model summary
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.023211 0.0223247 2.599242 26.18637 4e-07 1 -2620.07 5246.14 5261.16 7445.176 1102 1104

6 References

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Korhonen, Jori. 2020. “Coffee Processing Methods – Drying, Washing or Honey?” Barista Institute. https://www.baristainstitute.com/blog/jori-korhonen/january-2020/coffee-processing-methods-drying-washing-or-honey.

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